System and method for unsupervised prediction of machine failures

ABSTRACT

A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/US2016/065115 filed Dec. 6, 2016 which claims the benefit of U.S.Provisional Application No. 62/274,296 filed on Jan. 3, 2016, thecontents of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to maintenance systems formachines, and more specifically to monitoring machine operations forimproving machine processes.

BACKGROUND

Communications, processing, cloud computing, artificial intelligence,and other computerized technologies have advanced significantly inrecent years, heralding in new fields of technology and production.Further, many of the industrial technologies employed since or beforethe 1970s are still used today. Existing solutions related to theseindustrial technologies have typically seen minor improvements, therebyincreasing production and yield only slightly.

In modern manufacturing practices, manufacturers must often meet strictproduction timelines and provide flawless or nearly flawless productionquality. As a result, these manufacturers risk heavy losses whenever anunexpected machine failure occurs. A machine failure is an event thatoccurs when a machine deviates from correct service. Errors, which aretypically deviations from the correct state of the machine, are notnecessarily failures, but may lead to and indicate potential futurefailures. Besides failures, errors may otherwise cause unusual machinebehavior that may affect performance.

The average failure-based machine downtime for typical manufacturers(i.e., the average amount of time in which production shuts down, eitherin part or in whole, due to machine failure) is 17 days per year, i.e.,17 days of lost production and, hence revenue. In the case of a typical450 megawatt power turbine, for example, a single day of downtime cancost a manufacturer over $3 million US in lost revenue. Such downtimemay have additional costs related to repair, safety precautions, and thelike.

In energy power plants, billions of US dollars are spent annually onensuring reliability. Specifically, billions of dollars are spent onbackup systems and redundancies utilized to minimize productiondowntimes. Additionally, monitoring systems may be utilized to identifyfailures quickly, thereby speeding up the return to production whendowntime occurs. However, existing monitoring systems typically identifyfailures only after or immediately before downtime begins.

Further, existing solutions for monitoring machine failures typicallyrely on a set of predetermined rules for each machine. These rules setsdo not account for all data that may be collected with respect to themachine, and may only be used for checking particular key parameterswhile ignoring the rest. Moreover, these rules sets must be provided inadvance by engineers or other human analysts. As a result, only some ofthe collected data may be actually used by existing solutions, therebyresulting in wasted use of computing resources related to transmission,storage, and processing of unused data. Further, failure to consider allrelevant data may result in missed or otherwise inaccurate determinationof failures.

Additionally, existing solutions often rely on periodic testing atpredetermined intervals. Thus, even existing solutions that can predictfailures in advance typically return requests to perform machinemaintenance even when the machine is not in immediate danger of failing.Such premature replacement results in wasted materials and expensesspent replacing parts that are still functioning properly. Further, suchexisting solutions often determine failures only after failure occurs.As a result, such failures may not be prevented, resulting in down timeand lost revenue.

Further, existing monitoring and maintenance solutions often requirededicated testing equipment. Consequently, these solutions typicallyrequire specialized operators who are well-trained in the operation ofeach monitoring and maintenance system. Requiring specialized operatorscan be inconvenient and costly, and may introduce potential sources ofhuman error. Additionally, given the sheer amount of data that may becollected for any given machine in addition to minute fluctuations indata, a human analyst is not capable of adequately determining upcomingfailures.

It would therefore be advantageous to provide a solution that wouldovercome the challenges noted above.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor to delineate the scope of anyor all aspects. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term “someembodiments” may be used herein to refer to a single embodiment ormultiple embodiments of the disclosure.

Certain embodiments disclosed herein include a method for unsupervisedprediction of machine failures. The method comprises: monitoring sensoryinputs related to at least one machine; analyzing, via at leastunsupervised machine learning, the monitored sensory inputs, wherein theoutput of the unsupervised machine learning includes at least oneindicator; identifying, based on the at least one indicator, at leastone pattern; and determining, based on the at least one pattern and themonitored sensory inputs, at least one machine failure prediction.

Certain embodiments disclosed herein also include a non-transitorycomputer readable medium having stored thereon instructions for causinga processing circuitry to perform a process, the process comprising:monitoring sensory inputs related to at least one machine; analyzing,via at least unsupervised machine learning, the monitored sensoryinputs, wherein the output of the unsupervised machine learning includesat least one indicator; identifying, based on the at least oneindicator, at least one pattern; and determining, based on the at leastone pattern and the monitored sensory inputs, at least one machinefailure prediction.

Certain embodiments disclosed herein also include a system forunsupervised prediction of machine failures. The system comprises: aprocessing circuitry; and a memory, the memory containing instructionsthat, when executed by the processing circuitry, configure the systemto: monitor sensory inputs related to at least one machine; analyze, viaat least unsupervised machine learning, the monitored sensory inputs,wherein the output of the unsupervised machine learning includes atleast one indicator; identify, based on the at least one indicator, atleast one pattern; and determine, based on the at least one pattern andthe monitored sensory inputs, at least one machine failure prediction.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out anddistinctly claimed in the claims at the conclusion of the specification.The foregoing and other objects, features, and advantages of thedisclosed embodiments will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 is a network diagram utilized to describe the various disclosedembodiments.

FIG. 2 is a schematic diagram of a machine failure predictor systemaccording to an embodiment.

FIGS. 3A and 3B are simulations illustrating modeling of sensory inputs.

FIG. 4 is a simulation illustrating a general model of a plurality ofmeta-models.

FIG. 5 is a flowchart illustrating a method for predicting machinefailures according to an embodiment.

FIG. 6 is a flowchart illustrating a method for unsupervised detectionof anomalies according to an embodiment.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

The various disclosed embodiments include a method and system forunsupervised prediction of mechanical, electrical, or other failuresthat require maintenance. Various embodiments disclosed herein includeidentifying behavioral patterns of a machine via real-time monitoring ofindustrial data. At least sensory inputs associated with a machine arereceived from a plurality of sensors configured to capture data relatedto operation of the machine. The at least sensory inputs are analyzedvia unsupervised machine learning to predict future failure of themachine, timings for machine maintenance, or both. The analysis mayinclude comparing the sensory inputs to sensory inputs stored in adatabase.

The analysis may further include modeling the sensory inputs anddetecting indicators in the sensory inputs. The modeling may includegenerating meta-models for each component or portion of the machine. Themeta-models are monitored to detect indicators therein. Based on theindicators, machine failures may be predicted. In a further embodiment,machine maintenance times may be determined based on the predictedfailures, the indicators, or both.

FIG. 1 shows an example network diagram 100 utilized to describe thevarious disclosed embodiments. The example network diagram 100 includesa machine monitoring system (MMS) 130, a machine failure predictor 140,a database 150, and a client device 160 communicatively connected via anetwork 110. The example network diagram 100 further includes aplurality of sensors 120-1 through 120-n (hereinafter referred toindividually as a sensor 120 and collectively as sensors 120, merely forsimplicity purposes), communicatively connected to the machinemonitoring system 130. The network 110 may be, but is not limited to, awireless, a cellular or wired network, a local area network (LAN), awide area network (WAN), a metro area network (MAN), the Internet, theworldwide web (WWW), similar networks, and any combination thereof.

The client device 160 may be, but is not limited to, a personalcomputer, a laptop, a tablet computer, a smartphone, a wearablecomputing device, or any other device capable of receiving anddisplaying notifications indicating maintenance and failure timingpredictions, results of unsupervised analysis of machine operation data,or both.

The sensors 120 are located in proximity (e.g., physical proximity) to amachine 170. The machine 170 may be any machine for which performancecan be represented via sensory data such as, but not limited to, aturbine, an engine, a welding machine, a three dimensional (3D) printer,an injection molding machine, a combination thereof, a portion thereof,and the like. Each sensor 120 is configured to collect sensory inputssuch as, but not limited to, sound signals, ultrasound signals, light,movement tracking indicators, temperature, energy consumptionindicators, and the like based on operation of the machine 170. Thesensors 120 may include, but are not limited to, sound capturingsensors, motion tracking sensors, energy consumption meters, temperaturemeters, and the like. Any of the sensors 120 may be, but are notnecessarily, communicatively or otherwise connected to the machine 170(such connection is not illustrated in FIG. 1 merely for the sake ofsimplicity and without limitation on the disclosed embodiments).

The sensors 120 are communicatively connected to the machine monitoringsystem 130. The machine monitoring system 130 may be configured to storeand to preprocess sensory inputs received from the sensors 120.Alternatively or collectively, the machine monitoring system 130 may beconfigured to periodically retrieve collected sensory inputs stored in,for example, the database 150. The preprocessing may include, but is notlimited to, timestamping sensory inputs, de-trending, rescaling, noisefiltering, a combination thereof, and the like.

The preprocessing may further include feature extraction. The results ofthe feature extraction may include features to be utilized by themachine failure predictor 140 during unsupervised machine learning inorder to detect indicators. The feature extraction may include, but isnot limited to, dimension reduction techniques such as, but not limitedto, singular value decompositions, discrete Fourier transformations,discrete wavelet transformations, line segment methods, or a combinationthereof. When such dimension reduction techniques are utilized, thepreprocessing may result in, e.g., a lower-dimensional space for thesensory inputs. The machine monitoring system 130 is configured to sendthe preprocessed sensory inputs to the machine failure predictor 140.

In an embodiment, the machine failure predictor 140 is configured toreceive, via the network 110, the preprocessed sensory inputs associatedwith the machine 170 from the machine monitoring system 130. The sensoryinputs may be received continuously, and may be received in real-time.

In an embodiment, the machine failure predictor 140 may further storethe sensory input data received from the machine monitoring system 130.Alternatively or collectively, the sensory input data may be stored inthe database 150. The database 150 may further store sensory inputs(raw, preprocessed, or both) collected from a plurality of other sensors(not shown) associated with other machines (also not shown). Thedatabase 150 may further store indicators, anomalous patterns, failurepredictions, behavioral models utilized for analyzing sensory inputdata, or a combination thereof.

In an embodiment, the machine failure predictor 140 is configured toanalyze the preprocessed sensory inputs. The analysis may include, butis not limited to, unsupervised machine learning. In a furtherembodiment, the unsupervised machine learning may include one or moresignal processing techniques, implementation of one or more neuralnetworks, or both. It should be noted that different parametersrepresented by the sensory inputs may be analyzed using differentmachine learning techniques. For example, a temperature parameter may beanalyzed by applying a first machine learning technique to sensoryinputs from a temperature sensor, and an energy consumption parametermay be analyzed by applying a second machine learning technique tosensory inputs from an energy consumption gage.

In an embodiment, the machine failure predictor 140 may be configured toautomatically select at least one optimal method for detectingindicators in the sensory input data based on, e.g., a type of one ormore portions of the data. In a further embodiment, the selection may bebased on results from applying a plurality of models to each at least aportion of the sensory input data. In yet a further embodiment, theselection may be based further on a number of false positives in theresults.

In a further embodiment, the machine failure predictor 140 is configuredto generate a meta-model based on at least one portion of the machine170. Each portion of the machine for which a meta-model is generated maybe a component (not shown) such as, but not limited to, a pipe, anengine, a portion of an engine, a combination thereof, and the like.Generating a meta-model may include, but is not limited to, selecting amodel that optimally indicates anomalies in the sensory inputs for eachof the at least one portion of the machine 170. Each of the generatedmeta-models is utilized to detect anomalies in the behavior of therespective portion of the machine 170.

In an embodiment, the machine failure predictor 140 is configured togenerate, in real-time, at least one adaptive threshold for detectinganomalies based on the analysis. In a further embodiment, the machinefailure predictor 140 is configured to determine, in real-time, normalbehavior patterns for the sensory inputs of the machine 170 or eachportion thereof. The adaptive thresholds may be generated based on thedetermined normal behavior patterns. Generation of adaptive thresholdsfor detecting anomalies based on normal behavior patterns is describedfurther herein below with respect to FIGS. 3A and 3B.

In an embodiment, based on the detected anomalies, suspected errors maybe determined. In a further embodiment, when a suspected error isdetermined, the machine failure predictor 140 may be configured togenerate a notification indicating anomalous activity. In a furtherembodiment, the machine failure predictor 140 is further configured tosend the generated notification to, e.g., the user device 160.Alternatively or collectively, the machine failure predictor 140 may beconfigured to send the notification to a system (not shown) configuredto automatically mitigate failures.

It should be noted that the machine monitoring system 130 is shown inFIG. 1 as a separate component from the machine failure predictor 140merely for simplicity purposes and without limitation on the disclosedembodiments. The machine monitoring system 130 may be incorporated inthe machine failure predictor 140 so as to allow the machine failurepredictor 140 to obtain and preprocess sensory inputs without departingfrom the scope of the disclosure.

It should also be noted that the embodiments described herein above withrespect to FIG. 1 are discussed with respect to a user device 160 and amachine 170 merely for simplicity purposes and without limitation on thedisclosed embodiments. Multiple user devices may receive informationrelated to machine maintenance and failures without departing from thescope of the disclosure. Additionally, sensory inputs related tomultiple machines may be collected to determine failures of any or allof the machines without departing from the scope of the disclosure.

It should be further noted that the embodiments disclosed herein are notlimited to the specific architecture illustrated in FIG. 1 and otherarchitectures may be equally used without departing from the scope ofthe disclosed embodiments. Specifically, the machine failure predictor140 may reside in the cloud computing platform, a datacenter, onpremise, and the like. Moreover, in an embodiment, there may be aplurality of management servers operating as described hereinabove andconfigured to either have one as a standby proxy to take control in acase of failure, to share the load between them, or to split thefunctions between them.

FIG. 2 shows an example block diagram of the machine failure predictor140 implemented according to one embodiment. The machine failurepredictor 140 includes a processing circuitry 210 coupled to a memory220, a storage 230, a network interface 240, and a machine learning (ML)unit 250. In an embodiment, the components of the machine failurepredictor 140 may be communicatively connected via a bus 260.

The processing circuitry 210 may be realized as one or more hardwarelogic components and circuits. For example, and without limitation,illustrative types of hardware logic components that can be used includefield programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), Application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), general-purpose microprocessors,microcontrollers, digital signal processors (DSPs), and the like, or anyother hardware logic components that can perform calculations or othermanipulations of information.

The memory 220 may be volatile (e.g., RAM, etc.), non-volatile (e.g.,ROM, flash memory, etc.), or a combination thereof. In oneconfiguration, computer readable instructions to implement one or moreembodiments disclosed herein may be stored in the storage 230.

In another embodiment, the memory 220 is configured to store software.Software shall be construed broadly to mean any type of instructions,whether referred to as software, firmware, middleware, microcode,hardware description language, or otherwise. Instructions may includecode (e.g., in source code format, binary code format, executable codeformat, or any other suitable format of code). The instructions, whenexecuted by the one or more processors, cause the processing circuitry210 to perform the various processes described herein. Specifically, theinstructions, when executed, cause the processing circuitry 210 toperform predictions of machine maintenance as described herein.

The storage 230 may be magnetic storage, optical storage, and the like,and may be realized, for example, as flash memory or other memorytechnology, CD-ROM, Digital Versatile Disks (DVDs), or any other mediumwhich can be used to store the desired information.

The network interface 240 allows the machine failure predictor 140 tocommunicate with the machine monitoring system 130 for the purpose of,for example, receiving preprocessed sensory inputs. Additionally, thenetwork interface 240 allows the machine failure predictor 140 tocommunicate with the user device 160 in order to send, e.g.,notifications related to anomalous activity.

The machine learning unit 250 is configured to perform unsupervisedmachine learning based on sensory inputs received via the networkinterface 240 as described further herein. In an embodiment, the machinelearning unit 250 is further configured to determine, based on theunsupervised machine learning, predictions for failures of the machine170. In a further embodiment, the machine learning unit 250 is alsoconfigured to determine at least one recommendation for avoiding ormitigating the determined predicted failures. As a non-limiting example,the at least one recommendation may indicate that an exhaust pipe on themachine 170 should be replaced with a new exhaust pipe to avoid failure.

It should be understood that the embodiments described herein are notlimited to the specific architecture illustrated in FIG. 2, and otherarchitectures may be equally used without departing from the scope ofthe disclosed embodiments.

FIG. 3A is an example simulation illustrating determining behavioralpatterns implemented according to an embodiment. The simulation shown inFIG. 3A includes a graph 300A in which sensory inputs are represented bya curve 310A. In the example simulation shown in FIG. 3, the curve 310Arepresents an aggregated behavior of the sensory inputs over time.During operation of a machine (e.g., the machine 170, FIG. 1), theaggregated behavior represented by the curve 310A may be continuouslymonitored for repeated sequences such as repeated sequences 320A and330A. Upon determination of, for example, the repeated sequence 320A,the repeated sequence 330A, or both, a model of a normal behaviorpattern of the machine is generated. It should be noted that continuousmonitoring of, e.g., two or more cycles of behavior may be useful fordetermining more accurate patterns. As monitoring and, consequently,learning, continue, the normal behavior model may be updatedaccordingly. The models of normal behavior patterns may be utilized todetermine machine failure predictions. As a non-limiting example, if thesequence 320A preceded a machine failure, then the determination ofrepeated sequence 330A may be predicted to precede a machine failure.

FIG. 3B is an example simulation 300B illustrating generation ofadaptive thresholds. Based on one or more repeated sequences (e.g., therepeated sequences 320A and 330A), a maximum threshold 310B and aminimum threshold 320B are determined. The thresholds 310B and 320B maybe determined in real-time and regardless of past machine behavior. Inan example implementation, the thresholds 310B and 320B are dynamic andadapted based on the sequences 320A and 330A as well as any subsequentlydetermined sequences. The point 330B represents an indicator, i.e., adata point that is above the maximum threshold 310B or below the minimumthreshold 320B. Upon determination that one of the thresholds 310B or320B has been exceeded, an anomaly may be detected.

FIG. 4 is an example simulation 400 illustrating generating a model of amachine based on a plurality of meta-models. In the example simulation400, a machine (e.g., the machine 170) including three components isbeing monitored, where the three components are represented by themeta-models 410-1, 410-2, and 410-3, respectively. The meta-models arebased on sensory inputs related to their respective components, and maybe utilized to identify anomalies in the operation of each respectivecomponent of the machine. Based on the meta-models 410-1 through 410-3,a model 420 that is an optimal representation of the machine may begenerated.

FIG. 5 is an example flowchart 500 illustrating a method forunsupervised prediction of machine failures according to an embodiment.In an embodiment, the method may be performed by the machine failurepredictor 140.

At S510, sensory inputs related to a machine (e.g., the machine 170) aremonitored and analyzed to detect anomalies. The analysis may include,but is not limited to, unsupervised machine learning using preprocessedsensory inputs. The outputs of the unsupervised machine learning processinclude anomalies. In a further embodiment, S510 may include generatinga periodic anomalies map of the detected anomalies. Detecting anomaliesbased on sensory inputs is described further herein below with respectto FIG. 6.

At S520, the detected anomalies are correlated. In an embodiment, thedetected anomalies may be correlated with respect to each type ofsensory input. In a further embodiment, S520 may further include rankingcorrelated groups of sensory inputs. In another embodiment, S520 mayinclude applying a correlation function using a correlation coefficientsuch as, but not limited to, the Pearson correlation coefficient, theKendal correlation coefficient, or the Spearman correlation coefficient.

At S530, patterns in the correlated anomalies are identified.Identifying the patterns may include, but is not limited to, identifiedat least one anomalous sequence for each type of sensory input. Eachidentified anomalous sequence is a sequence that includes a plurality ofanomalies and is repeated at least once in the correlated anomalies.

At S540, at least one machine failure prediction is determined. The atleast one machine failure prediction may be a prediction of failure ofthe machine or of any portion thereof (e.g., a component of themachine). In an embodiment, the failures are predicted based on similarpatterns of, e.g., anomalies. In a further embodiment, S540 may furtherinclude determining a required maintenance time that is before thepredicted failure time. The determined required maintenance time may beutilized to, e.g., generate a recommendation for avoiding or mitigatingfailure, where the recommendation indicates the latest time at which therecommendation should be implemented.

At S550, a notification is generated. The notification may indicate,e.g., the at least one machine failure prediction, the determinedrequired maintenance time, or both. The notification may be sent to aclient device, or may be sent to a system configured to automaticallyperform maintenance on the machine or a portion thereof.

FIG. 6 is an example flowchart S510 illustrating a method for detectinganomalies based on sensory inputs according to an embodiment.

At S610, sensory inputs from at least one sensor associated with amachine are preprocessed. In an embodiment, S610 may further includeretrieving raw sensory data, and extracting features from the rawsensory data. The extracted features may include, but are not limitedto, a reduced-dimension subset of the raw sensory data. In anotherembodiment, S610 may further include de-trending, rescaling, noisefiltering, or a combination thereof.

At S620, at least one model is selected. Each model is selected for oneof the parameters represented by the preprocessed sensory inputs.Selecting the models may include computing optimization of models foreach parameter.

At optional S630, it may be determined whether the selected models arevalidated and, if so, execution continues with S640; otherwise,execution continues with S620. In an embodiment, S630 may includeinjecting randomized anomalies into a dataset, running each selectedmodel using the dataset with the injected randomized anomalies, andcalculating accuracy measures based on the injected anomalies run ofeach model.

At S640, the selected models are run and monitored to detect anomalies.In an embodiment, S640 may include generating an anomalies map. In anembodiment, S640 may further include generating at least one normalbehavior pattern based on the running of the selected models. The normalbehavior models utilized to generate the anomalies map may be created asdescribed further herein above with respect to FIGS. 3A-3B and 4. Inanother embodiment, S640 may further include generating, in real-time,at least one adaptive threshold. The generated adaptive thresholds maybe utilized to determine anomalous data points.

The detected anomalies may include, but are not limited to, pointanomalies, contextual anomalies, and collective anomalies. Pointanomalies include a single data point that is above or below a threshold(e.g., an adaptive threshold as described herein above with respect toFIGS. 3A and 3B) difference with respect to all other data points. Acontextual anomaly includes one or more data points that deviate fromnormal behavior within a given context (e.g., a particular period oftime). A collective anomaly includes a plurality of data points thatdeviate from normal behavior of other groupings of data points.

It should be understood that any reference to an element herein using adesignation such as “first,” “second,” and so forth does not generallylimit the quantity or order of those elements. Rather, thesedesignations are generally used herein as a convenient method ofdistinguishing between two or more elements or instances of an element.Thus, a reference to first and second elements does not mean that onlytwo elements may be employed there or that the first element mustprecede the second element in some manner. Also, unless stated otherwisea set of elements comprises one or more elements.

As used herein, the phrase “at least one of” followed by a listing ofitems means that any of the listed items can be utilized individually,or any combination of two or more of the listed items can be utilized.For example, if a system is described as including “at least one of A,B, and C,” the system can include A alone; B alone; C alone; A and B incombination; B and C in combination; A and C in combination; or A, B,and C in combination.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various otherperipheral units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the disclosed embodiment and the concepts contributed by the inventorto furthering the art, and are to be construed as being withoutlimitation to such specifically recited examples and conditions.Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosed embodiments, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof. Additionally, it is intended that such equivalentsinclude both currently known equivalents as well as equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

What is claimed is:
 1. A method for unsupervised prediction of machinefailures, comprising: monitoring sensory inputs related to at least onemachine; analyzing, via at least unsupervised machine learning, themonitored sensory inputs, wherein the output of the unsupervised machinelearning includes at least one indicator and wherein analyzing themonitored sensory inputs further comprises preprocessing the monitoredsensory inputs, wherein the preprocessing includes extracting at leastone feature raw sensory data; selecting, based on the preprocessedsensory inputs, at least one unsupervised machine learning model from anavailable plurality of unsupervised machine learning models, wherein theunsupervised machine learning includes running the selected at least oneunsupervised machine learning model using the preprocessed sensoryinputs, wherein each selected at least one unsupervised machine learningmodel is selected for at least one parameter represented by thepreprocessed sensory inputs; generating, based on the at least oneindicator, an anomalies map; identifying, based on the at least oneindicator, at least one pattern; and determining, based on the at leastone pattern and the monitored sensory inputs, at least one machinefailure prediction.
 2. The method of claim 1, further comprising:correlating, for each type of sensory input of the monitored sensoryinputs, at least one of the at least one indicator, wherein the at leastone pattern is identified further based on the correlation.
 3. Themethod of claim 1, further comprising: determining, based on the atleast one machine failure prediction, at least one required maintenancetime.
 4. The method of claim 3, further comprising: generating, based onthe at least one machine failure prediction, at least one recommendationfor avoiding or mitigating failure.
 5. The method of claim 4, furthercomprising: generating a notification, wherein the notificationindicates the at least one of: the at least one machine failureprediction, the at least one required maintenance time, and the at leastone recommendation.
 6. The method of claim 1, further comprising:generating, based on the running of the selected at least oneunsupervised machine learning model, at least one normal behaviorpattern.
 7. A method for unsupervised prediction of machine failures noncomprising: monitoring sensory inputs related to at least one machine;analyzing, via at least unsupervised machine learning, the monitoredsensory inputs, wherein the output of the unsupervised machine learningincludes at least one indicator and wherein analyzing the monitoredsensory inputs further comprises preprocessing the monitored sensoryinputs, wherein the preprocessing includes extracting at least onefeature raw sensory data; selecting, based on the preprocessed sensoryinputs, at least one unsupervised machine learning model from anavailable plurality of unsupervised machine learning models, wherein theunsupervised machine learning includes running the selected at least oneunsupervised machine learning model using the preprocessed sensoryinputs, wherein each selected at least one unsupervised machine learningmodel is selected for at least one parameter represented by thepreprocessed sensory inputs; generating, based on the running of theselected at least one unsupervised machine learning model, at least onenormal behavior pattern; identifying, based on the at least oneindicator, at least one anomalous pattern; and determining, based on theat least one anomalous pattern and the monitored sensory inputs, atleast one machine failure prediction.
 8. A system for unsupervisedprediction of machine failures, comprising: a processing circuitry; anda memory, the memory containing instructions that, when executed by theprocessing circuitry, configure the system to: monitor sensory inputsrelated to at least one machine; analyze, via at least unsupervisedmachine learning, the monitored sensory inputs, wherein the output ofthe unsupervised machine learning includes at least one indicator andwherein analyzing the monitored sensory inputs further comprisespreprocessing the monitored sensory inputs, wherein the preprocessingincludes extracting at least one feature raw sensory data; select, basedon the preprocessed sensory inputs, at least one unsupervised machinelearning model from an available plurality of unsupervised machinelearning models, wherein the unsupervised machine learning includesrunning the selected at least one unsupervised machine learning modelusing the preprocessed sensory inputs, wherein each selected at leastone unsupervised machine learning model is selected for at least oneparameter represented by the preprocessed sensory inputs; generate,based on the running of the selected at least one unsupervised machinelearning model, at least one normal behavior pattern; generate, based onthe at least one indicator and the at least one normal behavior pattern,an anomalies map; and identify, based on the at least one indicator andthe anomalies map, at least one anomalous pattern; and determine, basedon the at least one anomalous pattern and the monitored sensory inputs,at least one machine failure prediction.
 9. The system of claim 8,wherein the system is further configured to: correlate, for each type ofsensory input of the monitored sensory inputs, at least one of the atleast one indicator, wherein the at least one anomalous pattern isidentified further based on the correlation.
 10. The system of claim 8,wherein the system is further configured to: determine, based on the atleast one machine failure prediction, at least one required maintenancetime.
 11. The system of claim 10, wherein the system is furtherconfigured to: generate, based on the at least one machine failureprediction, at least one recommendation for avoiding or mitigatingfailure.
 12. The system of claim 11, wherein the system is furtherconfigured to: generate a notification, wherein the notificationindicates the at least one of: the at least one machine failureprediction, the at least one required maintenance time, and the at leastone recommendation.
 13. The method of claim 1, wherein the at least onemachine failure prediction is a prediction of a future failure of themachine.
 14. The method of claim 7, further comprising: correlating, foreach type of sensory input of the monitored sensory inputs, at least oneof the at least one indicator, wherein the at least one anomalouspattern is identified further based on the correlation.
 15. The methodof claim 7, further comprising: determining, based on the at least onemachine failure prediction, at least one required maintenance time. 16.The method of claim 15, further comprising: generating, based on the atleast one machine failure prediction, at least one recommendation foravoiding or mitigating failure.
 17. The method of claim 16, furthercomprising: generating a notification, wherein the notificationindicates the at least one of: the at least one machine failureprediction, the at least one required maintenance time, and the at leastone recommendation.
 18. The method of claim 7 further comprising:generating, based on the at least one indicator, an anomalies map. 19.The method of claim 7, wherein the at least one machine failureprediction is a prediction of a future failure of the machine.
 20. Themethod of claim 8, wherein the at least one machine failure predictionis a prediction of a future failure of the machine.